Bisono, Indriati Njoto and Robinson, Andrew P. (2014) Spatial Bayesian Model for Maximum Temperature. International Journal of Applied Mathematics and Statistics, 53 (6). pp. 137-144. ISSN 0973-7545
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Abstract
A three-stage Bayesian spatial model is fitted to temperature extremes covering Tasmania. In the first stage, the data in each grid cell are assumed to follow a GEV distribution with particular parameters. In the second stage, each GEV parameter is assumed to follow a Normal distribution with mean structure comprising a fixed and random effect component. A usual regression model with covariates longitude, latitude and elevation is employed for the fixed effect component, and a conditional auto regressive (CAR) model is used for the random effect. The estimation of the posterior parameters was conducted by Monte Carlo method using a hybrid MCMC of Metropolis and Gibbs sampler algorithm. We found the spatial random effect successfully smoothed the shape parameters, so that credible intervals of return levels were well behaved.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | Bayesian, spatial, hierarchical model; CAR; extreme values. |
| Subjects: | H Social Sciences > HA Statistics |
| Divisions: | Faculty of Industrial Technology > Industrial Engineering Department |
| Depositing User: | Admin |
| Date Deposited: | 26 Jul 2015 03:37 |
| Last Modified: | 03 Sep 2024 11:16 |
| URI: | https://repository.petra.ac.id/id/eprint/17110 |
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